A new methodology to obtain fuzzy systems autonomously from training data

This paper presents an approach to obtain a fuzzy system automatically from numerical data. The identification of the fuzzy system structure (number of rules and membership functions in each input variable) and the optimization of the parameters defining it are performed jointly. Starting from an initially simple fuzzy system, the numbers of membership functions in the input domain and of rules are adapted in order to reduce the approximation error. This method has the advantage that it does not require the human expert's assistance since the input-output characteristics of the fuzzy system and its structure are obtained from the training examples.